



'''simple docstring'''





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument(
     "--original_config_file",
     type=str,
     required=True,
     help="The YAML config file corresponding to the original architecture.",
 )
 parser.add_argument(
     "--num_in_channels",
     default=None,
     type=int,
     help="The number of input channels. If `None` number of input channels will be automatically inferred.",
 )
 parser.add_argument(
     "--image_size",
     default=512,
     type=int,
     help=(
         "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
         " Base. Use 768 for Stable Diffusion v2."
     ),
 )
 parser.add_argument(
     "--extract_ema",
     action="store_true",
     help=(
         "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
         " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
         " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
     ),
 )
 parser.add_argument(
     "--upcast_attention",
     action="store_true",
     help=(
         "Whether the attention computation should always be upcasted. This is necessary when running stable"
         " diffusion 2.1."
     ),
 )
 parser.add_argument(
     "--from_safetensors",
     action="store_true",
     help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
 )
 parser.add_argument(
     "--to_safetensors",
     action="store_true",
     help="Whether to store pipeline in safetensors format or not.",
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")




 def 							lowerCamelCase	(					_snake_case	:    List[Any]							):





     '''simple docstring'''


     if string == "True":
         return True
     elif string == "False":
         return False
     else:
         raise ValueError(f'''could not parse string as bool {string}'''							)

 parser.add_argument(
     "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
 )

 parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

 SCREAMING_SNAKE_CASE__          =  parser.parse_args()

 SCREAMING_SNAKE_CASE__          =  download_controlnet_from_original_ckpt(
     checkpoint_path=args.checkpoint_path,
     original_config_file=args.original_config_file,
     image_size=args.image_size,
     extract_ema=args.extract_ema,
     num_in_channels=args.num_in_channels,
     upcast_attention=args.upcast_attention,
     from_safetensors=args.from_safetensors,
     device=args.device,
     use_linear_projection=args.use_linear_projection,
     cross_attention_dim=args.cross_attention_dim,
 )



 controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase     		=							logging.getLogger()

__UpperCamelCase     		=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						os.makedirs(snake_case   ,    exist_ok=snake_case  )
						UpperCamelCase__       =							{"source": "What is love ?", "target": "life"}
						UpperCamelCase__       =							{"train": 12, "val": 2, "test": 2}
						for split in ["train", "test", "val"]:
									for field in ["source", "target"]:
												UpperCamelCase__       =							"\n".join([contents[field]] * n_lines[split]  )
												with open(os.path.join(snake_case   ,    F'''{split}.{field}'''  )   ,    "w"  ) as f:
															f.write(snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = "pytorch"  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_auto_remove_tmp_dir()
						UpperCamelCase__       =							os.path.join(snake_case   ,    "output"  )
						UpperCamelCase__       =							os.path.join(snake_case   ,    "data"  )
						self._create_dummy_data(data_dir=snake_case  )

						UpperCamelCase__       =							F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

						if gpus > 0:
									testargs.append(F'''--gpus={gpus}'''  )
									if is_apex_available():
												testargs.append("--fp16"  )
						else:
									testargs.append("--gpus=0"  )
									testargs.append("--distributed_backend=ddp_cpu"  )
									testargs.append("--num_processes=2"  )

						UpperCamelCase__       =							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
						execute_subprocess_async(snake_case   ,    env=self.get_env()  )

						UpperCamelCase__       =							os.path.join(snake_case   ,    "metrics.json"  )
						with open(snake_case  ) as f:
									UpperCamelCase__       =							json.load(snake_case  )
						return result




			@require_torch_gpu
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )




			@require_torch_multi_gpu
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=2  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )




			@require_torch_gpu
			@require_ray
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1   ,    distributed_retriever="ray"  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )







			@require_torch_multi_gpu
			@require_ray
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1   ,    distributed_retriever="ray"  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )


